No abstract

Psychological experiments on children's development of spatial knowledge suggest that experience at self-locomotion and visual tracking are important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to track a target object mentally (i.e., maintaining a representation of an object's position when outside the field of view) as a model for spatial development. Mental tracking is considered as prediction of an object's position, given the previous environmental state and motor commands and the current environment state resulting from movement. Following Jordan and Rumelhart's (1992) forward modeling architecture, the system consists of two components: an inverse model of sensory input to desired motor commands and a forward model of motor commands to desired sensory input (goals). The robot was tested on the "three cups"paradigm (in which children are required, under various movement conditions, to select the cup containing the hidden object). Consistent with child development, in the absence of the capacity for self-locomotion, the robot makes errors that are self-center-based. When given the ability for self-locomotion, the robot responds allocentrically.

Highly-functional mobile devices, such a smart phone, have appeared. Location based services of the mobile devices are assimilated in a variety of ways. Then, Indoor localization sensor is necessary to access the location based services seamlessly. This paper researched the performance of indoor localization with ZigBee based particle filter. This paper showed this method can localize a resting target within 2.0 meter accuracy and can localize a moving target with an area levels localizarion. ZigBee based particle filter can be one of some options for location based services in indoor spaces.

No abstract